JPEG2000 as a defense against attacks using adversarial examples

Date

2018-10-11

Author

Gündüz, Ayşe Elvan
Temizel, Alptekin
Taşkaya Temizel, Tuğba

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Adversarial examples have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These examples are generated by adding non-random noise to the test samples in order to fool the classifier. Adversarial attacks use these intentionally generated examples and they pose a security risk to the machine learning based systems. It has recently been shown that JPEG compression is effective against such attacks and classification accuracy on adversarial images is recovered when compression is increased. However, the accuracy drops when quantization becomes too aggressive. In this work, we propose using JPEG2000 as a defense mechanism as does not cause blocking artifacts and allow higher compression

URI

https://hdl.handle.net/11511/87113

DOI

https://doi.org/10.13140/RG.2.2.11531.13605

Conference Name

GPU Technology Conference,9 - 11 October 2018

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

A. E. Gündüz, A. Temizel, and T. Taşkaya Temizel, “JPEG2000 as a defense against attacks using adversarial examples ,” presented at the GPU Technology Conference,9 - 11 October 2018, Munich, Germany, 2018, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/87113.